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Source apportionment of ambient fine and coarse particulate matter polycyclic aromatic hydrocarbons at the Bertha Ganter-Fort McKay community site in the Oil Sands Region of Alberta, Canada

2019· article· en· W2912694725 on OpenAlexfundaboutno aff
Matthew S. Landis, William B. Studabaker, Joseph Patrick Pancras, Joseph R. Graney, Emily M. White, Eric S. Edgerton

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersWood Buffalo Environmental AssociationAcademia Oamenilor de Știință din România
KeywordsParticulatesEnvironmental chemistryEnvironmental scienceAerosolPolycyclic aromatic hydrocarbonApportionmentOil sandsAir pollutionEnrichment factorEnvironmental engineeringChemistryHeavy metalsArchaeology

Abstract

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A comprehensive filter-based particulate matter polycyclic aromatic hydrocarbon (PAH) source apportionment study was conducted at the Wood Buffalo Environmental Association Bertha Ganter-Fort McKay (BGFM) community monitoring station from 2014 to 2015 to quantify ambient concentrations and identify major sources. The BGFM station is located in close proximity to several surface oil sands production facilities and was previously found to be impacted by their air emissions. 24-hour integrated PM2.5 and PM10–2.5 samples were collected on a 1-in-3-day schedule yielding 108 complete organic/inorganic filter sets for source apportionment modeling. During the study period PM2.5 averaged 8.6 ± 11.8 μg m−3 (mean ± standard deviation), and PM10–2.5 averaged 8.5 ± 9.5 μg m−3. Wind regression analysis indicated that the oil sands production facilities were significant sources of PM2.5 mass and black carbon (BC), and that wildland fires were a significant source of the highest PM2.5 (>10 μg m−3) and BC events. A six-factor positive matrix factorization (PMF) model solution explained 95% of the measured PM2.5 and 78% of the measured ΣPAH. Five sources significantly contributed to PM2.5 including: Biomass Combustion (3.57 μg m−3; 40%); Fugitive Dust (1.86 μg m−3; 28%); Upgrader Stack Emissions (1.44 μg m−3; 21%); Petrogenic PAH (1.20 μg m−3; 18%); and Transported Aerosol (0.43 μg m−3 and 6%). However, the analysis indicated that only the pyrogenic PAH source factor significantly contributed (78%) to the measured ΣPAH. A five-factor PMF model dominated by fugitive dust sources explained 98% of PM10–2.5 mass and 86% of the ΣPAH. The predominant sources of PM10–2.5 mass were (i) Haul Road Dust (4.82 μg m−3; 53%), (ii) Mixed Fugitive Dust (2.89 μg m−3; 32%), (iii) Fugitive Oil Sand (0.88 μg m−3; 10%), Mobile Sources (0.23 μg m−3; 2%), and Organic Aerosol (0.06 μg m−3; 1%). Only the Organic Aerosol source significantly contributed (86%) to the measured ΣPAH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.217
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2019
Admission routes2
Has abstractyes

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